Detection performance of cooperative spectrum sensing with hard decision fusion in fading channels

Nallagonda, Srinivas; Chandra, Aniruddha; Roy, Sanjay Dhar; Kundu, Sumit; Kukolev, Pavel; Prokeš, Aleš

doi:10.1080/00207217.2015.1036369

Cited by 52 publications

(23 citation statements)

References 30 publications

(38 reference statements)

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“…is the Marcum's Q function and γ r c is the MRC combiner output SNR. Using [, (17)], we obtain

\begin{matrix} {\bar{Q}}_{d, r c, R a y} & = {(\frac{1}{1 + \bar{γ}})}^{N} \sum_{n = 1}^{u - 1} {(\frac{λ}{2})}^{n} \frac{\exp (- λ / 2)}{n!} \\ \times_{1} F_{1} [N; n + 1; \frac{\bar{γ} λ}{2 (1 + \bar{γ})}] + \frac{\bar{γ}}{1 + \bar{γ}} \exp [- \frac{λ}{2 (1 + \bar{γ})}] \\ \times [\sum_{k = 0}^{N - 2} {(\frac{1}{1 + \bar{γ}})}^{k} L_{k} [- \frac{λ \bar{γ}}{2 (1 + \bar{γ})}] \\ + (\frac{1 + \bar{γ}}{\bar{γ}}) {(\frac{1}{1 + \bar{γ}})}^{N - 1} L_{N - 1} [- \frac{λ \bar{γ}}{2 (1 + \bar{γ})}]] \end{matrix}

<...>…”

Section: Detection Probability Analysismentioning

confidence: 99%

“…In case of MRC scheme,

{\overset{Q}{true}}_{d, r c, N a k}

can be evaluated, by substituting Q d of MRC scheme given in and PDF of MRC combiner output SNR in as

\begin{matrix} {\bar{Q}}_{d, N a k, r c} & = \frac{{(m / \bar{γ})}^{N m}}{Γ (N m)} \int_{0}^{\infty} Q_{u} (\sqrt{2 γ_{r c}}, \sqrt{λ}) \\ \times γ_{r c}^{N m - 1} \exp (- \frac{m γ_{r c}}{\bar{γ}}) d γ_{r c} \end{matrix}

Using [, (17)], the end expression of can be obtained as

\begin{matrix} {\bar{Q}}_{d, r c, N a k} & = {(\frac{m}{m + \bar{γ}})}^{N m} \sum_{n = 1}^{u - 1} \frac{λ^{n} \exp (- λ / 2)}{2^{n} n!} \\ \times_{1} F_{1} [N m; n + 1; \frac{\bar{γ} λ}{2 (m + \bar{γ})}] + \frac{\bar{γ}}{m + \bar{γ}} \end{matrix}

…”

Section: Detection Probability Analysismentioning

confidence: 99%

“…Using [34,Section 8.970], it is verified that (11), (13), and (18) reduce to (5), (7), and (10), respectively, for m D 1. Another interesting observation is that the expression given in (18) do not contain any infinite series as opposed to [32, (33)].…”

Section: Rayleigh Fading Channelmentioning

confidence: 99%

“…Hoyt (or Nakagami-q) fading channel. The average Q d under SLS scheme, N Q d;ls;Hoy , can be obtained using [13] as…”

Section: 23mentioning

confidence: 99%

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Analytical performance of soft data fusion‐aided spectrum sensing in hybrid terrestrial‐satellite networks

Nallagonda¹,

Chandra

Roy

et al. 2016

Satell Commun Network

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Summary The cognitive radio (CR) technology is developed to help secondary users to utilize spectrum holes of the primary users (PUs) opportunistically. Accurate detection of spectrum holes via cooperative spectrum sensing (CSS) is essential for realization of CR protocols in next generation hybrid terrestrial‐satellite networks. Soft data fusion refers to a class of efficient CSS schemes for CR networks to find presence or absence of a PU. Each CR user senses the PU and transmits its sensing information in terms of energy obtained using energy detector to fusion center (FC). Next, any of the soft data schemes, which differ in technique of combining the energy information, may be performed at FC to acquire a final decision about a PU. In the present paper, numerical analysis of various soft data‐aided CSS schemes in different fading/shadowing environments is studied. In particular, closed‐form expressions of detection probability for the known soft data schemes are derived for each of the popular fading models. A comparative performance of soft combining schemes is illustrated for various network and channel conditions. Further, an optimal detection threshold that minimizes the total error is also indicated for different soft schemes. Copyright © 2016 John Wiley & Sons, Ltd.

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“…is the Marcum's Q function and γ r c is the MRC combiner output SNR. Using [, (17)], we obtain

\begin{matrix} {\bar{Q}}_{d, r c, R a y} & = {(\frac{1}{1 + \bar{γ}})}^{N} \sum_{n = 1}^{u - 1} {(\frac{λ}{2})}^{n} \frac{\exp (- λ / 2)}{n!} \\ \times_{1} F_{1} [N; n + 1; \frac{\bar{γ} λ}{2 (1 + \bar{γ})}] + \frac{\bar{γ}}{1 + \bar{γ}} \exp [- \frac{λ}{2 (1 + \bar{γ})}] \\ \times [\sum_{k = 0}^{N - 2} {(\frac{1}{1 + \bar{γ}})}^{k} L_{k} [- \frac{λ \bar{γ}}{2 (1 + \bar{γ})}] \\ + (\frac{1 + \bar{γ}}{\bar{γ}}) {(\frac{1}{1 + \bar{γ}})}^{N - 1} L_{N - 1} [- \frac{λ \bar{γ}}{2 (1 + \bar{γ})}]] \end{matrix}

<...>…”

Section: Detection Probability Analysismentioning

confidence: 99%

“…In case of MRC scheme,

{\overset{Q}{true}}_{d, r c, N a k}

can be evaluated, by substituting Q d of MRC scheme given in and PDF of MRC combiner output SNR in as

\begin{matrix} {\bar{Q}}_{d, N a k, r c} & = \frac{{(m / \bar{γ})}^{N m}}{Γ (N m)} \int_{0}^{\infty} Q_{u} (\sqrt{2 γ_{r c}}, \sqrt{λ}) \\ \times γ_{r c}^{N m - 1} \exp (- \frac{m γ_{r c}}{\bar{γ}}) d γ_{r c} \end{matrix}

Using [, (17)], the end expression of can be obtained as

\begin{matrix} {\bar{Q}}_{d, r c, N a k} & = {(\frac{m}{m + \bar{γ}})}^{N m} \sum_{n = 1}^{u - 1} \frac{λ^{n} \exp (- λ / 2)}{2^{n} n!} \\ \times_{1} F_{1} [N m; n + 1; \frac{\bar{γ} λ}{2 (m + \bar{γ})}] + \frac{\bar{γ}}{m + \bar{γ}} \end{matrix}

…”

Section: Detection Probability Analysismentioning

confidence: 99%

Section: Rayleigh Fading Channelmentioning

confidence: 99%

“…Hoyt (or Nakagami-q) fading channel. The average Q d under SLS scheme, N Q d;ls;Hoy , can be obtained using [13] as…”

Section: 23mentioning

confidence: 99%

See 2 more Smart Citations

Analytical performance of soft data fusion‐aided spectrum sensing in hybrid terrestrial‐satellite networks

Nallagonda¹,

Chandra

Roy

et al. 2016

Satell Commun Network

View full text Add to dashboard Cite

show abstract

“…However, the mixture of both CFAR and CDR threshold selection approaches has been employed to improve the throughput of CU and providing sufficient protection to PU from CU in additive white Gaussian noise (AWGN) environment 16 under noncooperative scenario. [23][24][25] However, Jafarian and Hamdi 26 employed double threshold selection approach to improve the detection probability of EDSS in cooperative environment. 20,21 Therefore, the cooperation between CUs is used to improve the credibility of sensing results.…”

Section: Introductionmentioning

confidence: 99%

Intelligent threshold selection in fading environment of cognitive radio network: Advances in throughput and total error probability

Kumar

Thakur

Pandit

et al. 2019

Int J Communication

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One of the main challenges of spectrum sensing (SS) in the cognitive radio network (CRN) is selection of threshold for captivating the sensing decision, and such an issue becomes more challenging especially at low signal-to-noise ratio (SNR) because it affects the sensing performance and throughput of cognitive user (CU). However, the sensing decision of individual CU may not be accurate due to fading effects in the sensing environment, and significant improvement is accomplished with the cooperation of users. Therefore, in this paper, we have investigated the performance of energy detector SS (EDSS) technique in the CRN under fading environment with cooperative and noncooperative SS scenario. Further, we have introduced the concept of critical SNR (γ c ) to select the appropriate threshold and based on which the algorithms are proposed to enhance the throughput and reduce the total error (sensing error) probability of CU at different SNR (γ). From the results, we have accomplished that at the low SNR region (γ ≤ γ c ), throughput is maximum with constant false-alarm rate (CFAR) threshold selection approach in the cooperative scenario while in the high SNR region (γ > γ c ), its value is maximum with minimizing the error probability (MEP) threshold selection approach in the noncooperative scenario. Moreover, the sensing error is reduced with cooperation, and threshold selection with MEP approach outperforms CFAR approach for γ ≤ γ c . Further, the proposed approach outperforms the other approaches in terms of throughput and sensing error (total error) probability in the Rayleigh and Nakagami-m fading environments.

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Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network

Majumder

Manshahia

2022

Handbook of Intelligent Computing and Optimization for Sustainable Development

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Detection performance of cooperative spectrum sensing with hard decision fusion in fading channels

Cited by 52 publications

References 30 publications

Analytical performance of soft data fusion‐aided spectrum sensing in hybrid terrestrial‐satellite networks

Analytical performance of soft data fusion‐aided spectrum sensing in hybrid terrestrial‐satellite networks

Intelligent threshold selection in fading environment of cognitive radio network: Advances in throughput and total error probability

Optimal Quantizer and Machine Learning–Based Decision Fusion for Cooperative Spectrum Sensing in IoT Cognitive Radio Network

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